Video broadcasters such as network and cable TV companies receive a tremendous amount of video data from content providers and local TV stations for rebroadcast to viewers. Since the broadcasters did not originally generate the video themselves, they typically are not aware of its quality before receiving it. When poor quality video is distributed, however, end-user customers oftentimes complain directly to the broadcaster about poor quality video, regardless of the source of the degradation. Thus there is a continued need for these broadcasters and others to assess video and rate it on a subjective quality scale that comports with their “golden eyes” internal viewers and customers' assessments.
The video industry creates tools to predict subjective quality in video, but these tools have limitations. Generally these tools suffer when comparing cascaded-processed videos, for example those where video impairments may have been caused by more than once source, such as compressing, de-compressing, then re-compressing the video with different parameters. More importantly, because these tools are generally designed to measure degradation for a particular process, such as compression or transmission across an imperfect channel, the tools require access to the video in both the original and degraded forms to be able to make the comparison. An example process is described in U.S. Pat. No. 6,975,776, “Predicting Human Vision Perception and Perceptual Difference,” which is incorporated by reference herein. In the scenario where the broadcaster receives a video from a third party source, however, only a single video is available, with no reference to compare it to.
Side-to-side video comparison tools also struggle with generating accurate subjective quality measurements in situations where the original (reference) video is of such poor quality that even a very accurate reproduction has poor quality. For instance, when the original video has poor quality factors such as softness, noise, poor or no color, poor contrast, clipped whites and blacks, etc., even an extremely accurate copy will look poor, and draw complaints from end users.
Other prior art tools measure specific impairments such as block artifacts caused by macroblock border discontinuities, detail loss, softness, noise, then add them together to produce a Mean Opinion Score (MOS) of viewers. However, the accuracy of these methods is generally limited due to insufficiently accurate representations of the human vision response, both perceptive and cognitive.
Embodiments of the invention address these and other limitations of the prior art.